Unexpected losses are one of the biggest shortcomings of traditional risk transfer systems. Because traditional risk management and transfer systems are necessarily driven by statistical assessment or by prior history measuring methods, they are generally limited to dealing with measured or otherwise captured events that vary within parameters that have already been captured and experienced. One of the problems with this is that most large losses are caused by events that fall outside the bounds of normal experience, i.e., hundred-year floods and once-in-a-lifetime events, or casualties such as asbestos or lead poisoning. However, smaller variations may also be corruptive to the operation of automated risk transfer and/or management systems, since the pooled resources covering transferred risks typically need to be optimized. For example, if the likelihood of the occurrence of a risk event is so high, or the costs of the event so large, or the pooled resources are not properly minimized related to the transferred risks, the resulting resources to be pooled are large relative to the amount of protection achieved, i.e., risk transferred. It is then not likely that a risk-exposed element will transfer its risk to the corresponding risk-transfer system. For risk-transfer systems, the so-called loss ratio provides a measure for the operational stability of the system. The loss ratio is the ratio of total losses incurred, paid, and reserved in claims plus adjustment expenses due to maintaining the system, divided by the total pooled resources, e.g., premiums. Loss ratios for property and casualty insurance systems, e.g., motor car insurance, typically range from 40% to 60%. Such systems are collecting more premiums than the amount of resources transferred to cover losses. In contrast, risk-transfer systems that consistently experience high loss ratios will not be able to maintain long-term operation. In the prior art, the terms “permissible”, “target”, “balance point”, or “expected” loss ratio are used interchangeably to refer to the loss ratio necessary to fulfill the system's operational goal to maintain its operation.
Automated risk transfer systems and appropriate techniques are vastly employed and implemented in many prior art risk transfer systems and insurance technology systems. Thus, in the last decade, apart from the traditional channels of financing risks, alternative routes based on automated, self-sufficient risk transfer systems and/or insurance systems have been developed. Self-sufficiency or self-containment in the context of this document is directed to systems capable of automated, long-term operation without operational interruption due to unbalanced resources. Thus, self-sufficiency defines an operating state not requiring any aid, support, or interaction, for keeping up the operation, i.e., the system is able to provide for survival of its operation independent of any human interaction. Therefore, it is a type of operational autonomy of an automated system. On an operational automation scale, a system with totally self-sufficient operation does not need manual external adjustments for its operation to initiate or uphold its operation, i.e., it is able to work in operational autonomy. The present invention extends this technology to a multi-tier risk transfer structure with mutually and dynamically tuned triggers by interaction with externally measured or otherwise externally captured environmental parameters, thereby reinforcing the importance of developing automated systems allowing a self-sufficient operation. Tuned means that the trigger parameters of the two trigger layers are dynamically adapted and transferred between the triggers. As described, the layered trigger structure tied to externally occurring conditions and events allows for a new form of maintaining and ensuring long-term operation of automated, autonomous operable risk-transfer systems, and further optimizing the operation and pooled resources of the systems.
The automation of modern insurance systems has been largely concentrated on the problem of how risk-averse components can beneficially and automatically transfer their risks to an automated risk-management system. Since the underlying problem has a statistical nature, the likelihood of a risk transfer system being triggered by a risk event comes close to certainty over an appropriately long time horizon, and the operation of the system thus cannot be steered by the condition of measuring the occurrence of a risk event, but rather when such a risk event is measured. An optimized operation of a risk transfer or insurance system depends on its structure and tuning based on the ability to forecast future risk event measurements. The level of uncertainty is high, since it affects the risk transfer structure and operation of the system. To relieve this problem, one of the characteristics of risk transfer systems is the pooling of risks and risk transfers. In the prior art, the pooling of risk transfers can typically involve the grouping, selecting and filtering of various risk exposures, so that the law of large numbers can operate to provide a more accurate prediction of future losses. From a technical point of view, if the losses associated with risk transfer are more predictable, the operation and management of the actual risk transfers can be optimized. Additional risk transfer is another important element, where first risk transfer or insurance systems can optimize or stabilize operation by partially shifting pooled risks to a third system, as a second insurance system. In the prior art, automated risk transfer systems have been used for quite some time as a technical tool to manage the risk of uncertain losses, in particular to keep up the operation of functional, technical or business units. These days, significant risk exposure is associated with many aspects in the life and non-life sectors. Risk-exposed units, such as any kinds of objects, individuals, corporate bodies and/or legal entities, are necessarily confronted with many forms of active and passive risk management to hedge and protect against the risk of certain losses and events. The prior art addresses such risks of loss, for example, based on transferring and pooling the risk of loss from a plurality of risk-exposed entities to a dedicated pooling entity. In essence, this can be executed by effectively allocating the risk of loss to this pooling unit or entity in that the resources of associated units, which are exposed to a certain risk, are pooled. If one of the units is hit by an event that is linked to a transferred risk, the pooling entity directly intercepts the loss or damage caused by the event by transferring resources from the pooled resources to the affected unit. Resource pooling can be achieved by exchanging predefined quantities of resources with the resource-pooling system; e.g., payments or premiums that are to be paid for the transfer of the risk. This means that predefined resource quantities are exchanged for the other unit, thereby assuming the risk of loss. As described above, insurance systems can use automated, electronic-based resource pooling systems to pool the resources and risks of associated risk-exposed components. As mentioned above, to avoid operational instabilities, such resource pooling systems of an insurance system are often coupled to one or more subsequent risk-transfer systems in order to redistribute parts of their pooled risks. Correspondingly, a loss to be covered can be portioned or segmented by those coupled insurance systems.
Typically, risk associated with risk-exposed components can be broadly divided into three categories, i.e., expected risks, unexpected risks, and catastrophic risks. The systems covering expected risks can simply be based on setting an appropriate threshold value for a resource retention, which should equalize the quantity of pooled risks. Unexpected risks, e.g., operational risks, risk based on an excessively low selected retention level, or risks occurring out of IBNR losses, i.e., incurred but not [yet] reported, cover prospective as well as retrospective risks, including so-called adverse development cover (ADC). The last type of possible losses concerning catastrophic risks are technically even more difficult to capture, since they do not obey the law of large numbers. Traditional prior art systems are directed to catastrophic derivatives, securitization, and contingency financing, in particular to transfer risks by appropriate structures to the capital market. Due to the different characteristics of the risks to be captured, the prior art systems fail to cover different risk transfers from different categories, in particular since the operation of prior art systems needs to be specifically adapted and optimized to cope with specific risk characteristics. Thus, in the prior art, each specific type of risk event needs to be covered by a different risk transfer system or mechanism, which makes the operation and optimization of the risk cover difficult and confusing for risk-exposed components. The goal of minimizing the total risk exposure of a risk-exposed component and/or an insurance system under different boundary criteria, such as value at risk or conditional value at risk criteria, i.e., by finding the optimal balance between the benefit reducing the risk by purchasing reinsurance shares and the cost premiums of the redistributed insurance risk shares, is difficult to achieve. Therefore, one of the objects of the present invention addresses the technical problem of coupling two automated resource pooling systems with the goal of pooling the risk exposure of associated components and seeking better and more effective technical implementations based on an appropriate risk transfer and dynamic trigger structure covering the different aforementioned risk categories, i.e., that is broader in its applicable scope and easier to be placed.
The prior art specifies a plurality of systems addressing the above-mentioned problem. For example, US 2004/0236698 A1 describes a system for automated risk management trade between two coupled systems, in particular, an insurance system and a reinsurance system. This system provides for the transfer of premiums and loss payments directly between the risk-pooling systems. Further, the system allows for interactions between the two coupled systems, which allows for decision-making functions concerning reinsurance products. Another example of the known prior art in the field of automated risk transfer systems is US 2011/0112870 A1. US 2011/0112870 A1 discloses a system for determining a percentage for assigning, i.e., transferring, related risk in an insurance pool, wherein the transferred risks are shared via a secondary resource pooling system that is based on predefined transfer-specific conditions of a reinsurance contract. The system mainly allows for automatically providing information regarding losses, which is transferred to the captive resource pooling system in the insurer's system and the reinsurer's system. However, US 2011/0112870 A1 does not disclose a system and technical mechanism for determining the amount of the actual risk transfer or covering different risk categories. It is worth noting that nothing in the prior art provides a system for a risk transfer structure capable of covering different categories of risks, or even capable of using the different categories of risks to balance the pooled risks.
In summary, in the prior art, existing systems, whose operations are at least partially based on risk transfer schemes or structures, come in many different forms, often with very different objectives and operational approaches. However, typically, the range of schemes or structures of the prior art systems are specific to one particular risk, risk category, locality, sector or country. Moreover, there is no system capable of providing a dynamically floating retention based on changes in losses over multiple years. Furthermore, these systems do not provide a self-sustaining interaction with the environment, and do not provide means for self-adjustment of their operation, thus do not allow for a stable long-term operation of systems. In this context, it is important to note that the limitations of the prior art risk-transfer systems previously discussed are also driven by the lack of information, this problem also extending to the risk analysis, so that they must rely completely on the information provided to them. These same limitations also extend to all known efforts to analyze and/or simulate the impact of changes in the transferred risks. However, it is impossible to forecast the impact on risks with no prior information. The lack of information also limits simulation systems, such as dynamic analysis or the like, to protect against the impact of changes in the pooled risks. Similarly, the lack of quantitative information on the impact of risks has limited the usefulness of automated risk-transfer systems.